In gratitude for altruistic peer reviewers ‐ Reviewer and Associate Editor awards 2017
Bibliographic record
Abstract
When we discuss the peer review system, it is more often than not to criticise it. We hear about how the system is broken by the race to maximise publications placing unbearable weight on both the number of manuscripts submitted and the time constraints that scientists have to review. We hear about how increasingly difficult it is to find reviewers, and how shallow reviews do a disservice to science and let poor science get published. We also hear about the opposite: how peer review slows the process of science communication. This editorial is not about any of those points. Here, we want to celebrate the system, and the people behind it, that through altruistic behaviour make science more trustworthy. Altruism is a behaviour that benefits the group at the expense of the self, and it is characteristic of cohesive social systems. Reviewing and editing manuscripts is altruistic behaviour. Associate editors and reviewers, by silently donating their time to improve and filter the scientific literature, are an essential element of the glue that makes the scientific community function as more than the sum of its parts. At a time when science is under attack, we feel it is important to shine light on this mostly invisible, but critical function in the scientific process. Following our launch of the annual reviewer and associate editor awards in the December 2016 issue (McGill, Dornelas & Field, 2016), we are here announcing the 2017 awards. There are many reviewers that have contributed to making GEB papers. Without them, the journal wouldn't function. Each year we acknowledge every reviewer in the last issue of the year. This year we are highlighting six reviewers who stood out. All performed multiple reviews during the year and accepted more invitations to review than they declined. Importantly, beyond those criteria, these six reviewers stood out in terms of the insight they offered, and the depth and quality of their comments. They are: Volker Bahn, Joaquin Hortal, Cory Merow, Carsten Meyer, Luca Santini and Adam Tomasovych. The editorial process at GEB, relies heavily on associate editors, who act as a second filter beyond the Editor-in-Chief team, who identify suitable reviewers, and who themselves provide detailed comments on the manuscripts they handle. The editorial system we have in place at GEB demands a lot more involvement in each manuscript by the editors than is the case for many journals. We are lucky to have such an extraordinary group of associate editors, and we are grateful to them all for the work they do. Still, among this excellent team, we would like to celebrate three people who stood out over the past year: Amanda Bates, Allen Hurlbert and Petr Keil.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".